Version 1
: Received: 11 July 2023 / Approved: 12 July 2023 / Online: 12 July 2023 (12:41:34 CEST)
How to cite:
Chen, L.; Liu, S. Graph-Root: Prediction of Root-Associated Proteins in Plants Based on Graph Convolutional Network and Network Embedding Method. Preprints2023, 2023070848. https://doi.org/10.20944/preprints202307.0848.v1
Chen, L.; Liu, S. Graph-Root: Prediction of Root-Associated Proteins in Plants Based on Graph Convolutional Network and Network Embedding Method. Preprints 2023, 2023070848. https://doi.org/10.20944/preprints202307.0848.v1
Chen, L.; Liu, S. Graph-Root: Prediction of Root-Associated Proteins in Plants Based on Graph Convolutional Network and Network Embedding Method. Preprints2023, 2023070848. https://doi.org/10.20944/preprints202307.0848.v1
APA Style
Chen, L., & Liu, S. (2023). Graph-Root: Prediction of Root-Associated Proteins in Plants Based on Graph Convolutional Network and Network Embedding Method. Preprints. https://doi.org/10.20944/preprints202307.0848.v1
Chicago/Turabian Style
Chen, L. and Siyang Liu. 2023 "Graph-Root: Prediction of Root-Associated Proteins in Plants Based on Graph Convolutional Network and Network Embedding Method" Preprints. https://doi.org/10.20944/preprints202307.0848.v1
Abstract
The root system plays an irreplaceable role in plant growth. Its improvement can increase crop productivity. However, such system is still mysterious for us. The underlying mechanism has not been fully uncovered. The investigation on proteins related to the root system is an important means to complete this task. In the previous time, lack of root-related proteins makes it impossible to adopt machine learning methods for designing efficient models for the discovery of novel root-related proteins. Recently, a public database on root-related proteins was set up and machine learning methods can be applied in this field. In this study, we proposed a machine learning based model, named Graph-Root, for identification of root-related proteins. The features derived from protein sequences and one network were extracted, where the former features were processed by graph convolutional neural network and multi-head attention, and the later features abstracted the linkage between proteins. These features were fed into the fully connected layer to make prediction. The 5-fold cross-validation and independent tests suggested its good performance. It also outperformed the only one previous model, SVM-Root. Furthermore, the importance of each feature type and component in the proposed model was investigated.
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
